22,291 Results Found

Background: The term “medical mistrust” has increased in literary usage within the last ten years, but the term has not yet been fully conceptualized. This article analyzes the usage of the term “medical mistrust” in the extan...

With the rapid development of “Internet plus”, medical care has entered the era of big data. However, there is little research on medical big data (MBD) from the perspectives of bibliometrics and visualization. The substantive research on the basic a...

Medical image analysis is crucial for the efficient diagnosis of many diseases. Typically, hospitals maintain vast repositories of images, which can be leveraged for various purposes, including research. However, access to such image collections is l...

Medical image analysis research requires medical image datasets. Nevertheless, due to various impediments, researchers have been described as “data starved”. We hypothesize that implicit evolving community standards require researchers to use ever-gr...

Background/Objectives: Diagnostic reasoning is essential in clinical practice and medical education, yet it often becomes an automated process, making its cognitive mechanisms less visible. Despite the widespread use of electronic medical records, fe...

The integration of vision and language processing into a cohesive system has already shown promise with the application of large language models (LLMs) in medical image analysis. Their capabilities encompass the generation of medical reports, disease...

The contradiction between the supply and demand of public medical resources in China is serious. On the basis of the “graded diagnosis and treatment” model, the Chinese government divides the medical grade and adjusts the allocation of me...

Cognitive information systems create a new class of intelligent systems focused on semantic data analysis tasks. Such systems are based on cognitive resonance processes, which use a knowledge-based perception model, to analyze and semantically classi...

Medical images are acquired at different resolutions based on clinical goals or available technology. In general, however, high-resolution images with fine structural details are preferred for visual task analysis. Recognizing this significance, seve...

Medical tourism is among the fastest-growing segments of the global travel industry, and Taiwan’s internationally recognised healthcare system positions it well to compete in this market. Yet the companies best placed to lead this development h...

With the increase in insured patients and an aging population, managing the length of stay (LOS) for inpatients has become crucial for controlling medical costs. Analyzing the factors influencing LOS is necessary for effective management. Previous st...

The burnout syndrome is characterized by emotional exhaustion, depersonalization, and reduced personal achievement. Uncertainty exists about the prevalence of burnout among medical and surgical residents. Associations between burnout and gender, age,...

In the past years, deep neural networks (DNN) have become popular in many disciplines such as computer vision (CV), natural language processing (NLP), etc. The evolution of hardware has helped researchers to develop many powerful Deep Learning (DL) m...

Deep learning approaches have demonstrated great achievements in the field of computer-aided medical image analysis, improving the precision of diagnosis across a range of medical disorders. These developments have not, however, been immune to the ap...

Aiming to look at the problems of the unreasonable layout of medical facilities and low coverage of primary medical services. This paper selects tertiary grade A hospitals, general hospitals, specialized hospitals, community-level hospitals, clinics,...

Background and Objectives: Sustainable healthcare is fundamentally rooted in community medicine education. The COVID-19 pandemic disrupted global advancement in integrating traditional teaching and experiential learning. Additionally, an alarming dec...

The administration of intensive end-of-life care just before death in older patients has become a major policy concern, as it increases medical costs; however, care intensity does not necessarily indicate quality. This study aimed to describe the tem...

In the era of big data, medical data sharing has become an inevitable requirement to improve the quality and efficiency of medical services. To advance the progress of medical data sharing and expedite the circulation of data value, it becomes crucia...

In this study, thermogravimetric experiments combined with kinetic and thermodynamic analyses were performed to investigate the pyrolysis and co-pyrolysis mechanisms of four typical medical wastes, including swab sticks, polypropylene (PP), textiles,...

Self-supervised learning can reduce the dependence of 3D medical image analysis on expensive voxel-level annotations, but masked image modeling remains inefficient when uniform random masking is applied to volumetric data with large redundant backgro...

The science of statistics contributes to the development and application of tools for the design, analysis, and interpretation of empirical medical studies [...]

The aim of this study is to develop a convenient and effective entropy analysis method for assessing the efficiency of workload distribution among medical institution personnel. This research is based on a model for evaluating employee workload in co...

The aim of this meta-analysis was to evaluate the prevalence of COVID-19 vaccination among medical students worldwide. Three electronic databases, i.e., PubMed, Scopus, and Web of Science (WoS), were used to collect the related studies according to t...

In this study, using the medical expenditures of the Taiwanese government and gross domestic product (GDP) as variables, the wavelet analysis method was used to empirically study the correlations and lead-lag relationships in quarterly data in the pe...

The novel coronavirus disease (COVID-19) pandemic has brought a great deal of pressure for medical students, who typically show elevated anxiety rates. Our aim is to investigate the prevalence of anxiety in medical students during this pandemic. This...

Deep semi-supervised learning (DSSL) is a machine learning paradigm that blends supervised and unsupervised learning techniques to improve the performance of various models in computer vision tasks. Medical image classification plays a crucial role i...

To evaluate the efficacy and safety of medical expulsive therapy (MET) for ureteral stones in pediatric patients, Cochrane, PubMed, Web of Science, Scopus, and the reference list of retrieved studies were searched up to September 2022 to identify RCT...

Medical image processing and analysis techniques play a significant role in diagnosing diseases. Thus, during the last decade, several noteworthy improvements in medical diagnostics have been made based on medical image processing techniques. In this...

Recent advances in artificial intelligence have significantly improved performance in medical imaging tasks such as segmentation, quantification, and report generation. However, most existing solutions operate as static pipelines with limited adaptab...

Prevailing trends in the automotive and medical device industry, such as life cycle overarching configurability, connectivity, and automation, require an adaption of development processes, especially regarding the security and safety thereof. The cha...

Deep learning has revolutionised medical image analysis, offering the possibility of automated, efficient, and highly accurate diagnostic solutions. This article explores recent developments in deep learning techniques applied to medical imaging, inc...

The unsustainable take-make-dispose linear economy prevalent in healthcare contributes 4.4% to global Greenhouse Gas emissions. A popular but not yet widely-embraced solution is to remanufacture common single-use medical devices like electrophysiolog...

The management of medical waste (MW) is a critical global challenge, contributing to toxic effects on humans, environmental degradation, and economic burdens. Despite advancements, gaps remain in adopting sustainable waste disposal practices, with li...

This paper presents the details of our research and the activities involved. Japan is one of the most advanced countries in medicine worldwide. However, in terms of technology, knowledge sharing, and successor development, Japanese medicine lags behi...

Accurate medical images analysis plays a vital role for several clinical applications. Nevertheless, the immense and complex data volume to be processed make difficult the design of effective algorithms. The first aim of this paper is to examine this...

Dry Eye Disease (DED) is a multifactorial condition increasingly affecting young populations, including medical students, due to intensive screen use, prolonged near work, and demanding academic environments. This study aimed to estimate the prevalen...

Pre-hospital emergency medical service (PHEMS) is critical for the treatment outcomes of life-threatening injuries and time-sensitive illnesses. Response time, influenced by traffic conditions and the site planning of pre-hospital emergency medical f...

Ordered List Averaging (OLA) was previously introduced as a methodology for evaluating machine learning algorithms according to their ability to prioritize medically relevant feature subsets. The original methodology demonstrated, through Friedman&nd...

Performance evaluation is one of the most critical components in assuring the comprehensive development of e-learning in medical education (e-LMED). Although several studies evaluate performance in e-LMED, no study presently maps the rising scientifi...

Graph neural networks (GNNs) are increasingly used for medical imaging analysis and biological data modeling, where the integration of radiomics, topology, geometry, and generative artificial intelligence (AI) may improve representation learning from...

Background: At present, the validity and reliability evidence of the Malay version of the Smartphone Addiction Scale (SAS-M) is only available by exploratory factor analysis (EFA). The aim of this study is to validate and determine the psychometric p...

In the past years, Deep Neural Networks (DNNs) have become popular in many disciplines such as Computer Vision (CV), and the evolution of hardware has helped researchers to develop many powerful Deep Learning (DL) models to deal with several problems...

Following the integration of the urban residents’ medical insurance into the new rural cooperative medical insurance in 2016, China has now formed a basic medical insurance system with the urban workers’ basic medical insurance system and...

Background/Objectives Foods for special medical purposes are an important component of nutritional management in patients at risk of malnutrition or already affected by it. The aim of the study was to evaluate the sensory properties and sensory accep...

While the importance of explicitly identifying and considering contingent factors such as decision content and context is widely accepted as a way to ensure the validity of the decision analysis for the specific task at hand, few studies include this...

The manuscript conducts a comparative analysis to assess the impact of noise on medical images using a proposed threshold value estimation approach. It applies an innovative method for edge detection on images of varying complexity, considering diffe...

Although the sharing of data is an important part of multicenter biomedical AI, direct data sharing is hindered by privacy laws, institutional data silos, and restrained trust and cooperation between institutions. While federated learning offers an o...

The purpose of this paper is to propose a framework for cybersecurity risk management in telemedicine. The framework, which uses a bow-tie approach for medical image diagnosis sharing, allows the identification, analysis, and assessment of risks, con...

Background: Medical resident burnout is a critical threat to healthcare workforce sustainability, with physical activity (PA) posited as a protective factor. This bibliometric analysis maps the global research landscape on this topic from 2005 to 202...

Background/Objectives: Open-source artificial intelligence models (OSAIMs), such as BiomedCLIP, hold great potential for medical image analysis. While OSAIMs are increasingly utilized for general image interpretation, their adaptation for specialized...